{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "p = r'C:\\Users\\Kerwin\\Desktop\\9509765_202112261914367337.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(p,encoding='utf-8') as f :\n",
    "    data = np.loadtxt(f,str,delimiter=',',skiprows=1)\n",
    "    # print(data[:,3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_ = data[:,0]\n",
    "# print(time_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "degree_ = data[:,8]\n",
    "# print(degree_)\n",
    "count_master = 0\n",
    "count_doctor = 0\n",
    "# print(len(degree_))\n",
    "for i in range(0,len(degree_)):\n",
    "    if(degree_[i]=='\"A.硕士\"'):count_master+=1\n",
    "    else:count_doctor+=1\n",
    "print(count_master)\n",
    "print(count_doctor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = \"Doctor\",\"Master\"\n",
    "size = [58,14]\n",
    "explode = (0,0)\n",
    "fig1,ax1 = plt.subplots()\n",
    "ax1.pie(size,labels=labels,explode=explode,autopct='%1.1f%%',shadow=False,startangle=90)\n",
    "ax1.axis('equal')\n",
    "# ax1.set_title(\"参与对象人数分布图\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "grade_1 = data[:,9]\n",
    "# print(grade_1)\n",
    "freshman_ =  0\n",
    "for i in range(0,len(grade_1)):\n",
    "    if(grade_1[i]=='\"A.研一/博一\"'):freshman_+=1\n",
    "print(freshman_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "grade_2 = data[:,10]\n",
    "graduate_ = 0\n",
    "# print(grade_2)\n",
    "for i in range(0,len(grade_2)):\n",
    "    if(grade_2[i]=='\"B.毕业年\"'):graduate_+=1\n",
    "print(graduate_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "grade_3 = data[:,11]\n",
    "others_ = 0\n",
    "# print(grade_3)\n",
    "for i in range(0,len(grade_3)):\n",
    "    if(grade_3[i]=='\"C.其他\"'):others_+=1\n",
    "print(others_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = \"freshman\",\" graduate class\",\"others\"\n",
    "size = [64,3,5]\n",
    "explode = (0,0,0)\n",
    "fig1,ax1 = plt.subplots()\n",
    "ax1.pie(size,labels=labels,explode=explode,autopct='%1.1f%%',shadow=False,startangle=90)\n",
    "ax1.axis('equal')\n",
    "# ax1.set_title(\"参与对象人数分布图\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['\"3\"' '\"2\"' '\"3\"' ... '\"2\"' '\"3\"' '\"3\"']\n",
      " ['\"3\"' '\"4\"' '\"4\"' ... '\"5\"' '\"2\"' '\"2\"']\n",
      " ['\"4\"' '\"2\"' '\"3\"' ... '\"5\"' '\"5\"' '\"5\"']\n",
      " ...\n",
      " ['\"4\"' '\"2\"' '\"2\"' ... '\"4\"' '\"3\"' '\"1\"']\n",
      " ['\"3\"' '\"2\"' '\"3\"' ... '\"3\"' '\"3\"' '\"3\"']\n",
      " ['\"4\"' '\"3\"' '\"4\"' ... '\"3\"' '\"2\"' '\"3\"']]\n",
      "(72, 21)\n",
      "[[3 3 4 ... 4 3 4]\n",
      " [2 4 2 ... 2 2 3]\n",
      " [3 4 3 ... 2 3 4]\n",
      " ...\n",
      " [2 5 5 ... 4 3 3]\n",
      " [3 2 5 ... 3 3 2]\n",
      " [3 2 5 ... 1 3 3]]\n",
      "(21, 72)\n"
     ]
    }
   ],
   "source": [
    "problem = data[:,12:33]\n",
    "print(problem)\n",
    "print(problem.shape)\n",
    "for i in range(0,72):\n",
    "    for j in range(0,21):\n",
    "        if(problem[i,j]=='\"1\"'):problem[i,j]=1\n",
    "        if(problem[i,j]=='\"2\"'):problem[i,j]=2\n",
    "        if(problem[i,j]=='\"3\"'):problem[i,j]=3\n",
    "        if(problem[i,j]=='\"4\"'):problem[i,j]=4\n",
    "        if(problem[i,j]=='\"5\"'):problem[i,j]=5\n",
    "problem=problem.astype(np.int32) \n",
    "problem=problem.T \n",
    "# problem = int(problem)\n",
    "print(problem)\n",
    "print(problem.shape)\n",
    "# print(data[:,12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "def survey(labels_question,problem, category_names):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    results : dict\n",
    "        A mapping from question labels to a list of answers per category.\n",
    "        It is assumed all lists contain the same number of entries and that\n",
    "        it matches the length of *category_names*.\n",
    "    category_names : list of str\n",
    "        The category labels.\n",
    "    \"\"\"\n",
    "    labels = labels_question\n",
    "    data = problem\n",
    "    data_cum = data.cumsum(axis=1)\n",
    "    # category_colors = plt.get_cmap('RdYlGn')(\n",
    "    #     np.linspace(0.15, 0.85, data.shape[1]))\n",
    "    category_colors = ['r','g','b','w','y']\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.invert_yaxis()\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.set_xlim(0, np.sum(data, axis=1).max())\n",
    "\n",
    "    for i, (colname, color) in enumerate(zip(category_names, category_colors)):\n",
    "        widths = data[:, i]\n",
    "        starts = data_cum[:, i] - widths\n",
    "        rects = ax.barh(labels, widths, left=starts, height=0.5,\n",
    "                        label=colname, color=color)\n",
    "\n",
    "        # r, g, b, _ = color\n",
    "        # text_color = 'white' if r * g * b < 0.5 else 'darkgrey'\n",
    "        # ax.bar_label(rects, label_type='center', color=text_color)\n",
    "    ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1),\n",
    "              loc='lower left', fontsize='small')\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "category_names = ['Strongly disagree', 'Disagree','Neither agree nor disagree', 'Agree', 'Strongly agree']\n",
    "labels_question = ['Question 3','Question 4','Question 5','Question 6','Question 7','Question 8','Question 9','Question 10','Question 11','Question 12','Question 13','Question 14','Question 15','Question 16','Question 17','Question 18','Question 19','Question 20','Question 21','Question 22','Question 23']\n",
    "print(len(labels_question))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survey(labels_question,problem, category_names)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4905652b14e4b7eb92899b78ac499a22c488804455b27940a322fd82aaf71031"
  },
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit ('base': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
